Overview

Dataset statistics

Number of variables12
Number of observations158
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.9 KiB
Average record size in memory96.8 B

Variable types

Categorical2
Numeric10

Warnings

Country has a high cardinality: 158 distinct values High cardinality
Happiness Rank is highly correlated with Happiness Score and 5 other fieldsHigh correlation
Happiness Score is highly correlated with Happiness Rank and 5 other fieldsHigh correlation
Economy (GDP per Capita) is highly correlated with Happiness Rank and 3 other fieldsHigh correlation
Family is highly correlated with Happiness Rank and 3 other fieldsHigh correlation
Health (Life Expectancy) is highly correlated with Happiness Rank and 3 other fieldsHigh correlation
Freedom is highly correlated with Happiness Rank and 1 other fieldsHigh correlation
Dystopia Residual is highly correlated with Happiness Rank and 1 other fieldsHigh correlation
Happiness Rank is highly correlated with Happiness Score and 5 other fieldsHigh correlation
Happiness Score is highly correlated with Happiness Rank and 5 other fieldsHigh correlation
Economy (GDP per Capita) is highly correlated with Happiness Rank and 3 other fieldsHigh correlation
Family is highly correlated with Happiness Rank and 4 other fieldsHigh correlation
Health (Life Expectancy) is highly correlated with Happiness Rank and 3 other fieldsHigh correlation
Freedom is highly correlated with Happiness Rank and 2 other fieldsHigh correlation
Dystopia Residual is highly correlated with Happiness Rank and 1 other fieldsHigh correlation
Happiness Rank is highly correlated with Happiness Score and 3 other fieldsHigh correlation
Happiness Score is highly correlated with Happiness Rank and 3 other fieldsHigh correlation
Economy (GDP per Capita) is highly correlated with Happiness Rank and 2 other fieldsHigh correlation
Family is highly correlated with Happiness Rank and 1 other fieldsHigh correlation
Health (Life Expectancy) is highly correlated with Happiness Rank and 2 other fieldsHigh correlation
Freedom is highly correlated with Economy (GDP per Capita) and 4 other fieldsHigh correlation
Economy (GDP per Capita) is highly correlated with Freedom and 5 other fieldsHigh correlation
Happiness Rank is highly correlated with Freedom and 6 other fieldsHigh correlation
Happiness Score is highly correlated with Freedom and 7 other fieldsHigh correlation
Region is highly correlated with Freedom and 7 other fieldsHigh correlation
Dystopia Residual is highly correlated with Happiness Rank and 1 other fieldsHigh correlation
Generosity is highly correlated with RegionHigh correlation
Health (Life Expectancy) is highly correlated with Economy (GDP per Capita) and 4 other fieldsHigh correlation
Trust (Government Corruption) is highly correlated with Economy (GDP per Capita) and 2 other fieldsHigh correlation
Family is highly correlated with Freedom and 4 other fieldsHigh correlation
Country is uniformly distributed Uniform
Happiness Rank is uniformly distributed Uniform
Country has unique values Unique
Economy (GDP per Capita) has unique values Unique
Family has unique values Unique
Freedom has unique values Unique
Generosity has unique values Unique
Dystopia Residual has unique values Unique

Reproduction

Analysis started2021-05-15 15:25:09.217536
Analysis finished2021-05-15 15:25:19.522619
Duration10.31 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Country
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct158
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Mali
 
1
Algeria
 
1
Denmark
 
1
Poland
 
1
Chile
 
1
Other values (153)
153 

Length

Max length24
Median length7
Mean length8.189873418
Min length4

Characters and Unicode

Total characters1294
Distinct characters53
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique158 ?
Unique (%)100.0%

Sample

1st rowSwitzerland
2nd rowIceland
3rd rowDenmark
4th rowNorway
5th rowCanada

Common Values

ValueCountFrequency (%)
Mali1
 
0.6%
Algeria1
 
0.6%
Denmark1
 
0.6%
Poland1
 
0.6%
Chile1
 
0.6%
Sweden1
 
0.6%
Ukraine1
 
0.6%
Myanmar1
 
0.6%
Israel1
 
0.6%
Thailand1
 
0.6%
Other values (148)148
93.7%

Length

2021-05-15T20:55:19.842113image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
republic3
 
1.6%
united3
 
1.6%
south2
 
1.1%
cyprus2
 
1.1%
and2
 
1.1%
congo2
 
1.1%
belarus1
 
0.5%
ireland1
 
0.5%
bangladesh1
 
0.5%
benin1
 
0.5%
Other values (168)168
90.3%

Most occurring characters

ValueCountFrequency (%)
a201
15.5%
i114
 
8.8%
n106
 
8.2%
e83
 
6.4%
r77
 
6.0%
o75
 
5.8%
l48
 
3.7%
t47
 
3.6%
u44
 
3.4%
s40
 
3.1%
Other values (43)459
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1079
83.4%
Uppercase Letter183
 
14.1%
Space Separator28
 
2.2%
Open Punctuation2
 
0.2%
Close Punctuation2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a201
18.6%
i114
10.6%
n106
9.8%
e83
 
7.7%
r77
 
7.1%
o75
 
7.0%
l48
 
4.4%
t47
 
4.4%
u44
 
4.1%
s40
 
3.7%
Other values (16)244
22.6%
Uppercase Letter
ValueCountFrequency (%)
S20
 
10.9%
C17
 
9.3%
M15
 
8.2%
B14
 
7.7%
A13
 
7.1%
T11
 
6.0%
L10
 
5.5%
I9
 
4.9%
K9
 
4.9%
N8
 
4.4%
Other values (14)57
31.1%
Space Separator
ValueCountFrequency (%)
28
100.0%
Open Punctuation
ValueCountFrequency (%)
(2
100.0%
Close Punctuation
ValueCountFrequency (%)
)2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1262
97.5%
Common32
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a201
15.9%
i114
 
9.0%
n106
 
8.4%
e83
 
6.6%
r77
 
6.1%
o75
 
5.9%
l48
 
3.8%
t47
 
3.7%
u44
 
3.5%
s40
 
3.2%
Other values (40)427
33.8%
Common
ValueCountFrequency (%)
28
87.5%
(2
 
6.2%
)2
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1294
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a201
15.5%
i114
 
8.8%
n106
 
8.2%
e83
 
6.4%
r77
 
6.0%
o75
 
5.8%
l48
 
3.7%
t47
 
3.6%
u44
 
3.4%
s40
 
3.1%
Other values (43)459
35.5%

Region
Categorical

HIGH CORRELATION

Distinct10
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Sub-Saharan Africa
40 
Central and Eastern Europe
29 
Latin America and Caribbean
22 
Western Europe
21 
Middle East and Northern Africa
20 
Other values (5)
26 

Length

Max length31
Median length18
Mean length21.35443038
Min length12

Characters and Unicode

Total characters3374
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWestern Europe
2nd rowWestern Europe
3rd rowWestern Europe
4th rowWestern Europe
5th rowNorth America

Common Values

ValueCountFrequency (%)
Sub-Saharan Africa40
25.3%
Central and Eastern Europe29
18.4%
Latin America and Caribbean22
13.9%
Western Europe21
13.3%
Middle East and Northern Africa20
12.7%
Southeastern Asia9
 
5.7%
Southern Asia7
 
4.4%
Eastern Asia6
 
3.8%
Australia and New Zealand2
 
1.3%
North America2
 
1.3%

Length

2021-05-15T20:55:20.054559image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-15T20:55:20.132382image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
and73
15.1%
africa60
12.4%
europe50
10.4%
sub-saharan40
 
8.3%
eastern35
 
7.3%
central29
 
6.0%
america24
 
5.0%
caribbean22
 
4.6%
asia22
 
4.6%
latin22
 
4.6%
Other values (10)105
21.8%

Most occurring characters

ValueCountFrequency (%)
a466
13.8%
r341
 
10.1%
324
 
9.6%
n280
 
8.3%
e271
 
8.0%
t176
 
5.2%
i172
 
5.1%
d115
 
3.4%
s109
 
3.2%
u108
 
3.2%
Other values (19)1012
30.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2561
75.9%
Uppercase Letter449
 
13.3%
Space Separator324
 
9.6%
Dash Punctuation40
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a466
18.2%
r341
13.3%
n280
10.9%
e271
10.6%
t176
 
6.9%
i172
 
6.7%
d115
 
4.5%
s109
 
4.3%
u108
 
4.2%
o88
 
3.4%
Other values (8)435
17.0%
Uppercase Letter
ValueCountFrequency (%)
A108
24.1%
E105
23.4%
S96
21.4%
C51
11.4%
N24
 
5.3%
L22
 
4.9%
W21
 
4.7%
M20
 
4.5%
Z2
 
0.4%
Space Separator
ValueCountFrequency (%)
324
100.0%
Dash Punctuation
ValueCountFrequency (%)
-40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3010
89.2%
Common364
 
10.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a466
15.5%
r341
 
11.3%
n280
 
9.3%
e271
 
9.0%
t176
 
5.8%
i172
 
5.7%
d115
 
3.8%
s109
 
3.6%
u108
 
3.6%
A108
 
3.6%
Other values (17)864
28.7%
Common
ValueCountFrequency (%)
324
89.0%
-40
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3374
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a466
13.8%
r341
 
10.1%
324
 
9.6%
n280
 
8.3%
e271
 
8.0%
t176
 
5.2%
i172
 
5.1%
d115
 
3.4%
s109
 
3.2%
u108
 
3.2%
Other values (19)1012
30.0%

Happiness Rank
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct157
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.49367089
Minimum1
Maximum158
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2021-05-15T20:55:20.265932image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.85
Q140.25
median79.5
Q3118.75
95-th percentile150.15
Maximum158
Range157
Interquartile range (IQR)78.5

Descriptive statistics

Standard deviation45.7543631
Coefficient of variation (CV)0.5755724021
Kurtosis-1.199932134
Mean79.49367089
Median Absolute Deviation (MAD)39.5
Skewness0.0004184693238
Sum12560
Variance2093.461743
MonotonicityIncreasing
2021-05-15T20:55:20.394589image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
822
 
1.3%
1581
 
0.6%
501
 
0.6%
571
 
0.6%
561
 
0.6%
551
 
0.6%
541
 
0.6%
531
 
0.6%
521
 
0.6%
511
 
0.6%
Other values (147)147
93.0%
ValueCountFrequency (%)
11
0.6%
21
0.6%
31
0.6%
41
0.6%
51
0.6%
61
0.6%
71
0.6%
81
0.6%
91
0.6%
101
0.6%
ValueCountFrequency (%)
1581
0.6%
1571
0.6%
1561
0.6%
1551
0.6%
1541
0.6%
1531
0.6%
1521
0.6%
1511
0.6%
1501
0.6%
1491
0.6%

Happiness Score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct157
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.375734177
Minimum2.839
Maximum7.587
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2021-05-15T20:55:20.530244image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2.839
5-th percentile3.65585
Q14.526
median5.2325
Q36.24375
95-th percentile7.2977
Maximum7.587
Range4.748
Interquartile range (IQR)1.71775

Descriptive statistics

Standard deviation1.145010135
Coefficient of variation (CV)0.212996048
Kurtosis-0.7760749386
Mean5.375734177
Median Absolute Deviation (MAD)0.7665
Skewness0.09776909409
Sum849.366
Variance1.311048209
MonotonicityDecreasing
2021-05-15T20:55:20.657903image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.1922
 
1.3%
4.6421
 
0.6%
5.0981
 
0.6%
5.1291
 
0.6%
5.8891
 
0.6%
6.9371
 
0.6%
4.6941
 
0.6%
3.6811
 
0.6%
4.351
 
0.6%
6.6111
 
0.6%
Other values (147)147
93.0%
ValueCountFrequency (%)
2.8391
0.6%
2.9051
0.6%
3.0061
0.6%
3.341
0.6%
3.4651
0.6%
3.5751
0.6%
3.5871
0.6%
3.6551
0.6%
3.6561
0.6%
3.6671
0.6%
ValueCountFrequency (%)
7.5871
0.6%
7.5611
0.6%
7.5271
0.6%
7.5221
0.6%
7.4271
0.6%
7.4061
0.6%
7.3781
0.6%
7.3641
0.6%
7.2861
0.6%
7.2841
0.6%

Standard Error
Real number (ℝ≥0)

Distinct153
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04788474684
Minimum0.01848
Maximum0.13693
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2021-05-15T20:55:20.780575image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.01848
5-th percentile0.0310355
Q10.0372675
median0.04394
Q30.0523
95-th percentile0.07926
Maximum0.13693
Range0.11845
Interquartile range (IQR)0.0150325

Descriptive statistics

Standard deviation0.01714617856
Coefficient of variation (CV)0.3580718222
Kurtosis5.989346403
Mean0.04788474684
Median Absolute Deviation (MAD)0.00728
Skewness1.983439396
Sum7.56579
Variance0.0002939914391
MonotonicityNot monotonic
2021-05-15T20:55:20.913723image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.037512
 
1.3%
0.049342
 
1.3%
0.03782
 
1.3%
0.043942
 
1.3%
0.050512
 
1.3%
0.035531
 
0.6%
0.061071
 
0.6%
0.036071
 
0.6%
0.033281
 
0.6%
0.050691
 
0.6%
Other values (143)143
90.5%
ValueCountFrequency (%)
0.018481
0.6%
0.018661
0.6%
0.020431
0.6%
0.024241
0.6%
0.025961
0.6%
0.027991
0.6%
0.030771
0.6%
0.030841
0.6%
0.031071
0.6%
0.031351
0.6%
ValueCountFrequency (%)
0.136931
0.6%
0.110681
0.6%
0.108951
0.6%
0.098111
0.6%
0.094381
0.6%
0.087421
0.6%
0.086581
0.6%
0.080961
0.6%
0.078961
0.6%
0.078321
0.6%

Economy (GDP per Capita)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct158
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8461372152
Minimum0
Maximum1.69042
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2021-05-15T20:55:21.039891image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.186325
Q10.5458075
median0.910245
Q31.1584475
95-th percentile1.394645
Maximum1.69042
Range1.69042
Interquartile range (IQR)0.61264

Descriptive statistics

Standard deviation0.4031207785
Coefficient of variation (CV)0.4764248296
Kurtosis-0.8669864214
Mean0.8461372152
Median Absolute Deviation (MAD)0.30658
Skewness-0.3175746523
Sum133.68968
Variance0.1625063621
MonotonicityNot monotonic
2021-05-15T20:55:21.171539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.302321
 
0.6%
1.125551
 
0.6%
0.939291
 
0.6%
0.397531
 
0.6%
0.28521
 
0.6%
0.681331
 
0.6%
1.025641
 
0.6%
0.770421
 
0.6%
0.188471
 
0.6%
0.881131
 
0.6%
Other values (148)148
93.7%
ValueCountFrequency (%)
01
0.6%
0.01531
0.6%
0.016041
0.6%
0.06941
0.6%
0.07121
0.6%
0.07851
0.6%
0.083081
0.6%
0.174171
0.6%
0.188471
0.6%
0.190731
0.6%
ValueCountFrequency (%)
1.690421
0.6%
1.563911
0.6%
1.554221
0.6%
1.521861
0.6%
1.4591
0.6%
1.427271
0.6%
1.396511
0.6%
1.395411
0.6%
1.394511
0.6%
1.386041
0.6%

Family
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct158
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9910459494
Minimum0
Maximum1.40223
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2021-05-15T20:55:21.294211image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.415606
Q10.8568225
median1.02951
Q31.214405
95-th percentile1.3184715
Maximum1.40223
Range1.40223
Interquartile range (IQR)0.3575825

Descriptive statistics

Standard deviation0.272369086
Coefficient of variation (CV)0.2748299271
Kurtosis0.9188188118
Mean0.9910459494
Median Absolute Deviation (MAD)0.17851
Skewness-1.006893127
Sum156.58526
Variance0.07418491901
MonotonicityNot monotonic
2021-05-15T20:55:21.418877image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.183541
 
0.6%
1.122411
 
0.6%
1.025071
 
0.6%
1.118621
 
0.6%
1.260381
 
0.6%
0.855631
 
0.6%
1.285481
 
0.6%
0.302851
 
0.6%
0.985211
 
0.6%
0.679541
 
0.6%
Other values (148)148
93.7%
ValueCountFrequency (%)
01
0.6%
0.139951
0.6%
0.302851
0.6%
0.353861
0.6%
0.381741
0.6%
0.385621
0.6%
0.411341
0.6%
0.414111
0.6%
0.415871
0.6%
0.431061
0.6%
ValueCountFrequency (%)
1.402231
0.6%
1.369481
0.6%
1.360581
0.6%
1.349511
0.6%
1.340431
0.6%
1.330951
0.6%
1.322611
0.6%
1.319671
0.6%
1.318261
0.6%
1.313791
0.6%

Health (Life Expectancy)
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct157
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6302593671
Minimum0
Maximum1.02525
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2021-05-15T20:55:21.546046image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1515875
Q10.439185
median0.696705
Q30.8110125
95-th percentile0.942084
Maximum1.02525
Range1.02525
Interquartile range (IQR)0.3718275

Descriptive statistics

Standard deviation0.2470777663
Coefficient of variation (CV)0.3920255362
Kurtosis-0.3939350955
Mean0.6302593671
Median Absolute Deviation (MAD)0.159855
Skewness-0.7053284857
Sum99.58098
Variance0.0610474226
MonotonicityNot monotonic
2021-05-15T20:55:21.671218image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.923562
 
1.3%
0.414351
 
0.6%
0.81161
 
0.6%
0.514661
 
0.6%
0.809251
 
0.6%
0.708061
 
0.6%
0.70951
 
0.6%
0.698051
 
0.6%
0.697021
 
0.6%
0.538861
 
0.6%
Other values (147)147
93.0%
ValueCountFrequency (%)
01
0.6%
0.047761
0.6%
0.066991
0.6%
0.075661
0.6%
0.076121
0.6%
0.091311
0.6%
0.098061
0.6%
0.15011
0.6%
0.151851
0.6%
0.160071
0.6%
ValueCountFrequency (%)
1.025251
0.6%
1.013281
0.6%
0.991111
0.6%
0.965381
0.6%
0.955621
0.6%
0.954461
0.6%
0.947841
0.6%
0.945791
0.6%
0.941431
0.6%
0.931561
0.6%

Freedom
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct158
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4286149367
Minimum0
Maximum0.66973
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2021-05-15T20:55:21.790949image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.170474
Q10.32833
median0.435515
Q30.5490925
95-th percentile0.641588
Maximum0.66973
Range0.66973
Interquartile range (IQR)0.2207625

Descriptive statistics

Standard deviation0.1506927839
Coefficient of variation (CV)0.3515808037
Kurtosis-0.4607783896
Mean0.4286149367
Median Absolute Deviation (MAD)0.11246
Skewness-0.4134619729
Sum67.72116
Variance0.02270831513
MonotonicityNot monotonic
2021-05-15T20:55:21.914726image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.414661
 
0.6%
0.367721
 
0.6%
0.406721
 
0.6%
0.434771
 
0.6%
0.317671
 
0.6%
0.58451
 
0.6%
0.198471
 
0.6%
0.465821
 
0.6%
0.558841
 
0.6%
0.334571
 
0.6%
Other values (148)148
93.7%
ValueCountFrequency (%)
01
0.6%
0.076991
0.6%
0.092451
0.6%
0.100811
0.6%
0.103841
0.6%
0.11851
0.6%
0.121021
0.6%
0.156841
0.6%
0.172881
0.6%
0.18261
0.6%
ValueCountFrequency (%)
0.669731
0.6%
0.665571
0.6%
0.662461
0.6%
0.65981
0.6%
0.658211
0.6%
0.651241
0.6%
0.649381
0.6%
0.641691
0.6%
0.641571
0.6%
0.64041
0.6%

Trust (Government Corruption)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct157
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1434218354
Minimum0
Maximum0.55191
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2021-05-15T20:55:22.154086image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.015823
Q10.061675
median0.10722
Q30.180255
95-th percentile0.401446
Maximum0.55191
Range0.55191
Interquartile range (IQR)0.11858

Descriptive statistics

Standard deviation0.1200340736
Coefficient of variation (CV)0.8369302568
Kurtosis1.384786522
Mean0.1434218354
Median Absolute Deviation (MAD)0.052555
Skewness1.385462595
Sum22.66065
Variance0.01440817882
MonotonicityNot monotonic
2021-05-15T20:55:22.269777image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.325242
 
1.3%
0.399281
 
0.6%
0.193171
 
0.6%
0.053271
 
0.6%
0.071221
 
0.6%
0.129051
 
0.6%
0.100621
 
0.6%
0.179221
 
0.6%
0.002271
 
0.6%
0.105011
 
0.6%
Other values (147)147
93.0%
ValueCountFrequency (%)
01
0.6%
0.002271
0.6%
0.006491
0.6%
0.008721
0.6%
0.010311
0.6%
0.010781
0.6%
0.01141
0.6%
0.013971
0.6%
0.016151
0.6%
0.022991
0.6%
ValueCountFrequency (%)
0.551911
0.6%
0.522081
0.6%
0.49211
0.6%
0.483571
0.6%
0.438441
0.6%
0.429221
0.6%
0.419781
0.6%
0.413721
0.6%
0.399281
0.6%
0.385831
0.6%

Generosity
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct158
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2372955063
Minimum0
Maximum0.79588
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2021-05-15T20:55:22.397621image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.071195
Q10.1505525
median0.21613
Q30.3098825
95-th percentile0.4751735
Maximum0.79588
Range0.79588
Interquartile range (IQR)0.15933

Descriptive statistics

Standard deviation0.126684934
Coefficient of variation (CV)0.5338699244
Kurtosis1.746527654
Mean0.2372955063
Median Absolute Deviation (MAD)0.07702
Skewness1.001960576
Sum37.49269
Variance0.01604907251
MonotonicityNot monotonic
2021-05-15T20:55:22.547221image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.054441
 
0.6%
0.091311
 
0.6%
0.253281
 
0.6%
0.264751
 
0.6%
0.071721
 
0.6%
0.112511
 
0.6%
0.123881
 
0.6%
0.209511
 
0.6%
0.185571
 
0.6%
0.282141
 
0.6%
Other values (148)148
93.7%
ValueCountFrequency (%)
01
0.6%
0.001991
0.6%
0.026411
0.6%
0.054441
0.6%
0.055471
0.6%
0.058411
0.6%
0.064311
0.6%
0.068221
0.6%
0.071721
0.6%
0.077991
0.6%
ValueCountFrequency (%)
0.795881
0.6%
0.57631
0.6%
0.519121
0.6%
0.517521
0.6%
0.515351
0.6%
0.503181
0.6%
0.479981
0.6%
0.47611
0.6%
0.475011
0.6%
0.471791
0.6%

Dystopia Residual
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct158
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.098976772
Minimum0.32858
Maximum3.60214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2021-05-15T20:55:22.671889image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.32858
5-th percentile1.2365865
Q11.75941
median2.095415
Q32.462415
95-th percentile3.0374555
Maximum3.60214
Range3.27356
Interquartile range (IQR)0.703005

Descriptive statistics

Standard deviation0.5535497923
Coefficient of variation (CV)0.2637236389
Kurtosis0.5341213282
Mean2.098976772
Median Absolute Deviation (MAD)0.356215
Skewness-0.2389108094
Sum331.63833
Variance0.3064173726
MonotonicityNot monotonic
2021-05-15T20:55:22.786108image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.777291
 
0.6%
2.675851
 
0.6%
1.879961
 
0.6%
1.599271
 
0.6%
3.107121
 
0.6%
1.598881
 
0.6%
1.69441
 
0.6%
2.893191
 
0.6%
2.13091
 
0.6%
2.051251
 
0.6%
Other values (148)148
93.7%
ValueCountFrequency (%)
0.328581
0.6%
0.654291
0.6%
0.670421
0.6%
0.671081
0.6%
0.899911
0.6%
0.981951
0.6%
0.998951
0.6%
1.213051
0.6%
1.240741
0.6%
1.264621
0.6%
ValueCountFrequency (%)
3.602141
0.6%
3.260011
0.6%
3.191311
0.6%
3.177281
0.6%
3.107121
0.6%
3.107091
0.6%
3.088541
0.6%
3.051371
0.6%
3.0351
0.6%
2.893191
0.6%

Interactions

2021-05-15T20:55:09.612814image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:09.720527image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:09.808292image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:09.902139image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:09.999876image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:10.093626image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:10.186378image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:10.279130image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:10.374875image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:10.482584image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:10.583314image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:10.670082image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:10.750866image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:10.834644image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:10.921444image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:11.006847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:11.089625image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:11.172404image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:11.253292image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:11.341089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:11.423837image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:11.627292image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:11.709764image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:11.799523image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:11.895267image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:11.988019image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:12.075798image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:12.165558image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:12.252865image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:12.344586image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:12.433853image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:12.528599image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:12.618362image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:12.710116image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:12.826808image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:12.918072image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:13.009336image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:13.102597image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:13.195167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:13.290908image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:13.382661image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:13.476406image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:13.559160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:13.648955image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:13.742693image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:13.832428image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:13.920225image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:14.009954image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:14.100821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:14.313756image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:14.408504image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:14.499261image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:14.579078image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:14.664890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:14.752621image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:14.837932image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:14.921274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:15.007047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:15.093820image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:15.186567image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:15.284305image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:15.378086image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:15.460835image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:15.551619image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:15.644342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:15.735129image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:15.822894image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:15.913657image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:16.002419image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:16.100253image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:16.192608image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:16.284331image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:16.376603image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:16.477903image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:16.567662image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:16.655748image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:16.742003image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:16.951462image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:17.035266image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:17.124120image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:17.209922image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:17.306633image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:17.430326image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:17.537015image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:17.645756image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:17.741048image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:17.831810image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:17.926552image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:18.021369image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:18.118082image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:18.211862image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:18.302653image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:18.387741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:18.477525image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:18.573603image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:18.667350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:18.770075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:18.859867image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:18.945637image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-05-15T20:55:19.039386image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-05-15T20:55:22.895846image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-15T20:55:23.073380image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-15T20:55:23.251225image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-15T20:55:23.427320image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-05-15T20:55:19.240847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-15T20:55:19.444854image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

CountryRegionHappiness RankHappiness ScoreStandard ErrorEconomy (GDP per Capita)FamilyHealth (Life Expectancy)FreedomTrust (Government Corruption)GenerosityDystopia Residual
0SwitzerlandWestern Europe17.5870.034111.396511.349510.941430.665570.419780.296782.51738
1IcelandWestern Europe27.5610.048841.302321.402230.947840.628770.141450.436302.70201
2DenmarkWestern Europe37.5270.033281.325481.360580.874640.649380.483570.341392.49204
3NorwayWestern Europe47.5220.038801.459001.330950.885210.669730.365030.346992.46531
4CanadaNorth America57.4270.035531.326291.322610.905630.632970.329570.458112.45176
5FinlandWestern Europe67.4060.031401.290251.318260.889110.641690.413720.233512.61955
6NetherlandsWestern Europe77.3780.027991.329441.280170.892840.615760.318140.476102.46570
7SwedenWestern Europe87.3640.031571.331711.289070.910870.659800.438440.362622.37119
8New ZealandAustralia and New Zealand97.2860.033711.250181.319670.908370.639380.429220.475012.26425
9AustraliaAustralia and New Zealand107.2840.040831.333581.309230.931560.651240.356370.435622.26646

Last rows

CountryRegionHappiness RankHappiness ScoreStandard ErrorEconomy (GDP per Capita)FamilyHealth (Life Expectancy)FreedomTrust (Government Corruption)GenerosityDystopia Residual
148ChadSub-Saharan Africa1493.6670.038300.341930.760620.150100.235010.052690.183861.94296
149GuineaSub-Saharan Africa1503.6560.035900.174170.464750.240090.377250.121390.286571.99172
150Ivory CoastSub-Saharan Africa1513.6550.051410.465340.771150.151850.468660.179220.201651.41723
151Burkina FasoSub-Saharan Africa1523.5870.043240.258120.851880.271250.394930.128320.217471.46494
152AfghanistanSouthern Asia1533.5750.030840.319820.302850.303350.234140.097190.365101.95210
153RwandaSub-Saharan Africa1543.4650.034640.222080.773700.428640.592010.551910.226280.67042
154BeninSub-Saharan Africa1553.3400.036560.286650.353860.319100.484500.080100.182601.63328
155SyriaMiddle East and Northern Africa1563.0060.050150.663200.474890.721930.156840.189060.471790.32858
156BurundiSub-Saharan Africa1572.9050.086580.015300.415870.223960.118500.100620.197271.83302
157TogoSub-Saharan Africa1582.8390.067270.208680.139950.284430.364530.107310.166811.56726